We are addressing the key questions of:
| Median | Upper 50 CI | Lower 50 CI | |
|---|---|---|---|
| Peak Hospitalizations | 6,086.00 | 9,727.00 | 2,558.00 |
| Deaths by August 1, 2020 | 21,562.50 | 31,506.25 | 12,798.75 |
| Detected Illnesses by August 1,2020 | 622,988.00 | 900,654.75 | 362,311.00 |
| Total Illnesses by August 1, 2020 | 5,428,588.50 | 6,715,058.50 | 3,217,905.50 |
| Proportion of Cases Detected (%) | 13.22 | 16.04 | 10.45 |
| CFR Based on Observed Illnesses (%) | 3.87 | 4.74 | 2.78 |
| CFR Based on Total Illnesses (%) | 0.48 | 0.62 | 0.35 |
| R0 - before social distancing | 3.38 | 3.79 | 2.96 |
| % Reduction in Social Contacts (March 15 - ) | 59.82 | 54.26 | 63.59 |
Dashed line = Maximum possible capacity (i.e., total licensed hospital beds, ICU beds, ventilators) in L.A. County
Demonstrating model fit against COVID-19 data for Los Angeles, for the following variables:
COVID-19 data is shown as black dots in the figures below.
We analyze how population prevalence of known COVID-19 risk factors: advanced age, existence of other health conditions or comorbidities, smoking status, and obesity status, affect COVID-19 illness trajectories in L.A. County and spatial subdivisions.
First, we estimate the conditional probability of COVID illness severity given combinations of risk factors. We categorize the population into a number of risk profiles, representing different combinations of known COVID-19 risk factors: advanced age, existence of other health conditions or comorbidities, smoking status, and obesity status. Using previous COVID-19 studies reporting the marginal risk of severe COVID-19 outcomes given individual risk factors, we develop a statistical model to estimate the probability of COVID illness trajectories for individuals having or not having combinations of risk factors represented by these risk profiles. Specifically, we estimate the probability that individuals within a specific risk group are admitted to hospital given having acquired illness \(Pr(Hospital | Illness, Profile_i)\), are admitted to the ICU given admittance to hospitalized \(Pr(ICU | Hospital, Profile_i)\), and that die given being admitted to the ICU \(Pr(Death | ICU, Profile_i)\). More information is provided below under Methods and Data.
For the analysis below, we have grouped the multiple risk profiles into 5 key risk groups according to similar within-group levels of the probabilities \(Pr(Hospital | Illness, Profile_i)\), \(Pr(ICU | Hospital, Profile_i)\), and \(Pr(Death | ICU, Profile_i)\).
Second, we use these probabilities to estimate the proportion of each risk group that will make up the resulting cohorts of COVID patients admitted to hospital, admitted to ICU, or that die within the L.A. County population, based on the prevalence of each risk group in the population.
Results are also presented for each Service Planning Area (SPA) population within L.A. County. A SPA is a specific geographical region within Los Angeles County used by the Department of Public Health to plan and provide health services. There are 8 SPAs in Los Angeles County.
Here we summarize our estimated parameter values for key epidemic and model quantities:
Because our model is stochastic and we are using Bayesian techniques for parameter estimation, each posterior parameter estimate is represented by a distribution of likely values.
This table summarizes key statistics of each estimated parameter: the mean and the standard deviation (sd).
| R0 | Prop. cases detected (r) | Frac R0 Mar11 | Pr(Death|ICU) | Pr(Hospital|Illness) | Pr(ICU|Hospital) | Pr(Ventilation|ICU) | Frac R0 Apr23 | |
|---|---|---|---|---|---|---|---|---|
| mean | 3.36 | 0.14 | 0.42 | 0.54 | 0.21 | 0.36 | 0.74 | 0.40 |
| sd | 0.61 | 0.05 | 0.06 | 0.16 | 0.03 | 0.04 | 0.08 | 0.06 |
Information informing prior distribution - \(R0\) prior estimate is based on values for \(R0\) estimated from other published studies on COVID-19.
Information informing this parameter’s prior distribution:
We use mobility data to narrow the specification of the reduction in the average number of new infections due to an infected person (R0) in a completely susceptible population under recent social distancing restrictions.
Effectively, reductions in mobility correspond to a proportional reduction in R0
Reduction in mobility observed in LA County: Source: Assessing changes in commuting and individual mobility in major metropolitan areas in the United States during the COVID-19 outbreak
Our modeled reduction in R0 timeline:
We use previous studies to narrow the specification of the probability of hospitalization given illness, admittance to the intensive care unit (ICU) given being in hospital, ventilation given being in ICU, and death given being in ICU by incorporating risk factors, including age, sex, smoking and other comorbidities. The prevalence of these risk factors in Los Angeles County is also included.
Studies on COVID-19 clinical presentation and trajectories to inform the probability of hospitalization, ICU, and ventilation based on single risk factors: - Guan, Wei-jie, et al. “Clinical characteristics of coronavirus disease 2019 in China.” New England Journal of Medicine (2020). - Petrilli, Christopher M., et al. “Factors associated with hospitalization and critical illness among 4,103 patients with COVID-19 disease in New York City.” medRxiv (2020).
Prevalence data sources: - Los Angeles County Health Survey - UCLA California Health Information Survey